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pytorch写一个神经网络训练示例代码

当使用PyTorch训练全连接神经网络时，你需要定义神经网络模型、损失函数和优化器，并编写训练循环。下面是一个简单的示例，演示了如何使用PyTorch来训练一个全连接神经网络。

import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as data

# 定义全连接神经网络模型
class SimpleNet(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.fc2 = nn.Linear(hidden_size, output_size)
        
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 准备数据
features = torch.tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0]], dtype=torch.float)
labels = torch.tensor([0, 1, 0], dtype=torch.long)

# 创建自定义的Dataset
custom_dataset = data.TensorDataset(features, labels)

# 创建DataLoader
dataloader = data.DataLoader(custom_dataset, batch_size=2, shuffle=True)

# 初始化模型、损失函数和优化器
input_size = 3
hidden_size = 5
output_size = 2
model = SimpleNet(input_size, hidden_size, output_size)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.1)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    for inputs, targets in dataloader:
        # 前向传播
        outputs = model(inputs)
        loss = criterion(outputs, targets)
        
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# 使用训练好的模型进行预测
test_input = torch.tensor([[2.0, 3.0, 4.0]], dtype=torch.float)
with torch.no_grad():
    output = model(test_input)
    _, predicted = torch.max(output, 1)
    print('Predicted class:', predicted.item())